Whether you’re looking to transform and clean large volumes of data or collaborate with colleagues to build advanced analytics jobs that can be scaled and run automatically, Databricks offers a Unified Analytics Platform that promises to make your life easier.

Built by the same team who came up with Apache Spark, and benefitting from strong partnerships with both Microsoft Azure and AWS, Databricks is designed to take the pain out of managing your cloud-scale analytics platform, allowing you to focus on valuable analysis.

In the first of 2 recorded webcasts Thorogood consultants Andrew Kennedy and Leandro Eleuterio look at the first of two key use cases for Databricks:

Part 1. Databricks for Data Science

Using demos based on real customer use cases, they introduce some of Databricks’ key features for Data Science, like the ability to automatically scale the analysis based on the workload, and the option to switch between SQL, R and Python depending on the task at hand.

They look at how Databricks MLFlow supports the analytics project lifecycle, and consider how you can use it in combination with other tools to automate analytics and present outputs to the key decision-makers in your organization.